Homework #2

library(tidyverse)

## -- Attaching packages -------------------------------------------------------------------------------- tidyverse 1.2.1 --

## v ggplot2 3.2.1     v purrr   0.3.3
## v tibble  2.1.3     v dplyr   0.8.3
## v tidyr   1.0.0     v stringr 1.4.0
## v readr   1.3.1     v forcats 0.4.0

## Warning: package 'purrr' was built under R version 3.6.3

## -- Conflicts ----------------------------------------------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(readr)
library(RColorBrewer)
building_fires_original <- read_csv("building_fires.csv")

## Parsed with column specification:
## cols(
##   .default = col_character(),
##   IM_INCIDENT_KEY = col_double(),
##   UNITS_ONSCENE = col_double(),
##   TOTAL_INCIDENT_DURATION = col_double(),
##   ZIP_CODE = col_double(),
##   FIRE_ORIGIN_BELOW_GRADE_FLAG = col_double(),
##   STORY_FIRE_ORIGIN_COUNT = col_double(),
##   STANDPIPE_SYS_PRESENT_FLAG = col_double(),
##   lon = col_double(),
##   lat = col_double()
## )

## See spec(...) for full column specifications.

firehouses <- read_csv("FDNY_Firehouse_Listing.csv") %>%
  dplyr::filter(!is.na(Latitude))

## Parsed with column specification:
## cols(
##   FacilityName = col_character(),
##   FacilityAddress = col_character(),
##   Borough = col_character(),
##   Postcode = col_double(),
##   Latitude = col_double(),
##   Longitude = col_double(),
##   `Community Board` = col_double(),
##   `Community Council` = col_double(),
##   `Census Tract` = col_double(),
##   BIN = col_double(),
##   BBL = col_double(),
##   NTA = col_character()
## )

Location of Severe Fires

Provide a leaflet map of the highest severity fires (i.e. subset to the highest category in HIGHEST_LEVEL_DESC) contained in the file buiding_fires.csv. Ignore locations that fall outside the five boroughs of New York City. Provide at least three pieces of information on the incident in a popup.

library(leaflet)

#need to subset to fires of highest severity
building_fires <- building_fires_original %>%
  filter(HIGHEST_LEVEL_DESC == "7 - Signal 7-5")

variables <- paste("Date:", building_fires$INCIDENT_DATE_TIME, "<br/>",
                   "Incident Duration in seconds:", building_fires$TOTAL_INCIDENT_DURATION, "<br/>",
                   "Primary Action:", building_fires$ACTION_TAKEN1_DESC, "<br/>")

fire_map <- leaflet(building_fires) %>%
  addTiles() %>% 
  addCircles(lng = ~lon,
             lat = ~lat,
             color = "blue",
             popup = variables)
  
  
fire_map

Layers and Clusters

Color by Type of Property

Start with the previous map. Now, distinguish the markers of the fire locations by PROPERTY_USE_DESC, i.e. what kind of property was affected. If there are too many categories, collapse some categories. Choose an appropriate coloring scheme to map the locations by type of affected property. Add a legend informing the user about the color scheme. Also make sure that the information about the type of affected property is now contained in the popup information. Show this map.

#subsetting to locations that have more than 15 occurances and grouping into 5 categories


building_fires$PROPERTY_USE_DESC[building_fires$PROPERTY_USE_DESC == "000 - Property Use, other"] <- "Other"
building_fires$PROPERTY_USE_DESC[building_fires$PROPERTY_USE_DESC == "131 - Church, mosque, synagogue, temple, chapel"] <- "Other"
building_fires$PROPERTY_USE_DESC[building_fires$PROPERTY_USE_DESC == "700 - Manufacturing, processing"] <- "Other"
building_fires$PROPERTY_USE_DESC[building_fires$PROPERTY_USE_DESC == "800 - Storage, other"] <- "Other"
building_fires$PROPERTY_USE_DESC[building_fires$PROPERTY_USE_DESC == "881 - Parking garage, (detached residential garage)"] <- "Other"
building_fires$PROPERTY_USE_DESC[building_fires$PROPERTY_USE_DESC == "891 - Warehouse"] <- "Other"


building_fires$PROPERTY_USE_DESC[building_fires$PROPERTY_USE_DESC == "400 - Residential, other"] <- "Housing"
building_fires$PROPERTY_USE_DESC[building_fires$PROPERTY_USE_DESC == "419 - 1 or 2 family dwelling"] <- "Housing"
building_fires$PROPERTY_USE_DESC[building_fires$PROPERTY_USE_DESC == "429 - Multifamily dwelling"] <- "Housing"
building_fires$PROPERTY_USE_DESC[building_fires$PROPERTY_USE_DESC == "439 - Boarding/rooming house, residential hotels"] <- "Housing"
building_fires$PROPERTY_USE_DESC[building_fires$PROPERTY_USE_DESC == "449 - Hotel/motel, commercial"] <- "Housing"

building_fires$PROPERTY_USE_DESC[building_fires$PROPERTY_USE_DESC == "160 - Eating, drinking places, other"] <- "Food"
building_fires$PROPERTY_USE_DESC[building_fires$PROPERTY_USE_DESC == "161 - Restaurant or cafeteria"] <- "Food"
building_fires$PROPERTY_USE_DESC[building_fires$PROPERTY_USE_DESC == "162 - Bar or nightclub"] <- "Food"
building_fires$PROPERTY_USE_DESC[building_fires$PROPERTY_USE_DESC == "519 - Food and beverage sales, grocery store"] <- "Food"

building_fires$PROPERTY_USE_DESC[building_fires$PROPERTY_USE_DESC == "210 - Schools, non-adult, other"] <- "Education"

building_fires$PROPERTY_USE_DESC[building_fires$PROPERTY_USE_DESC == "311 - 24-hour care Nursing homes, 4 or more persons"] <- "Medical"
building_fires$PROPERTY_USE_DESC[building_fires$PROPERTY_USE_DESC == "331 - Hospital - medical or psychiatric"] <- "Medical"

building_fires$PROPERTY_USE_DESC[building_fires$PROPERTY_USE_DESC == "500 - Mercantile, business, other"] <- "Business"
building_fires$PROPERTY_USE_DESC[building_fires$PROPERTY_USE_DESC == "549 - Specialty shop"] <- "Business"
building_fires$PROPERTY_USE_DESC[building_fires$PROPERTY_USE_DESC == "564 - Laundry, dry cleaning"] <- "Business"
building_fires$PROPERTY_USE_DESC[building_fires$PROPERTY_USE_DESC == "579 - Motor vehicle or boat sales, services, repair"] <- "Business"
building_fires$PROPERTY_USE_DESC[building_fires$PROPERTY_USE_DESC == "599 - Business office"] <- "Business"

building_fires_condensed <- building_fires %>%
  filter(PROPERTY_USE_DESC == "Other" | 
           PROPERTY_USE_DESC =="Housing" | 
           PROPERTY_USE_DESC =="Food" | 
           PROPERTY_USE_DESC =="Medical" | 
           PROPERTY_USE_DESC =="Business")


pal = colorFactor("Set1", domain = building_fires_condensed$PROPERTY_USE_DESC) # Grab a palette
color_pal = pal(building_fires_condensed$PROPERTY_USE_DESC)

variables1 <- paste("Date:", building_fires$INCIDENT_DATE_TIME, "<br/>",
                   "Incident Duration in seconds:", building_fires$TOTAL_INCIDENT_DURATION, "<br/>",
                   "Primary Action:", building_fires$ACTION_TAKEN1_DESC, "<br/>",
                   "Property Type:", building_fires_condensed$PROPERTY_USE_DESC, "<br/>")

fire_map_color <- leaflet(building_fires_condensed) %>%
  addTiles() %>% 
  addCircles(lng = ~lon,
             lat = ~lat,
             color = color_pal,
             popup = variables1) %>%
  addLegend(pal = pal, values = ~building_fires_condensed$PROPERTY_USE_DESC, title = "Property Types")

fire_map_color

Cluster

Add marker clustering, so that zooming in will reveal the individual locations but the zoomed out map only shows the clusters. Show the map with clusters.

fire_map_cluster <- fire_map_color %>%
  addCircleMarkers(clusterOptions = markerClusterOptions())

## Assuming "lon" and "lat" are longitude and latitude, respectively

fire_map_cluster

Fire Houses

The second data file contains the locations of the 218 firehouses in New York City. Start with the non-clustered map (2b) and now adjust the size of the circle markers by severity (TOTAL_INCIDENT_DURATION or UNITS_ONSCENE seem plausible options). More severe incidents should have larger circles on the map. On the map, also add the locations of the fire houses. Add two layers (“Incidents”, “Firehouses”) that allow the user to select which information to show.

firehouses_map <- leaflet(building_fires) %>%
  addTiles() %>%
  addCircles(radius = building_fires$TOTAL_INCIDENT_DURATION/100)

## Assuming "lon" and "lat" are longitude and latitude, respectively

firehouses_map

firehouses_map2 <- leaflet() %>%
  addTiles() %>%
  addCircles(data = building_fires, 
             radius = building_fires$TOTAL_INCIDENT_DURATION/100,
             group = "Incidents") %>%
  addCircles(data = firehouses,
             group = "Firehouses",
             color = "red") %>%
  addLayersControl(
    baseGroups = c("Incidents",
                   "Firehouses"),
    position = "topright",
    options = layersControlOptions(collapsed = FALSE))

## Assuming "lon" and "lat" are longitude and latitude, respectively

## Assuming "Longitude" and "Latitude" are longitude and latitude, respectively

firehouses_map2

Distance from Firehouse and Response Time

We now want to investigate whether the distance of the incident from the nearest firehouse varies across the city.

Calculate Distance

For all incident locations (independent of severity), identify the nearest firehouse and calculate the distance between the firehouse and the incident location. Provide a scatter plot showing the time until the first engine arrived (the variables INCIDENT_DATE_TIME and ARRIVAL_DATE_TIME) will be helpful.

Now also visualize the patterns separately for severe and non-severe incidents (use HIGHEST_LEVEL_DESC but feel free to reduce the number of categories). What do you find?

library(geosphere)

## Warning: package 'geosphere' was built under R version 3.6.3

library(rgeos)

## Warning: package 'rgeos' was built under R version 3.6.2

## Loading required package: sp

## rgeos version: 0.5-2, (SVN revision 621)
##  GEOS runtime version: 3.6.1-CAPI-1.10.1 
##  Linking to sp version: 1.4-0 
##  Polygon checking: TRUE

distance <- distm(firehouses, building_fires_original, fun = distGeo)

## Warning in .pointsToMatrix(x): NAs introduced by coercion

## Warning in .pointsToMatrix(y): NAs introduced by coercion

## Error in .pointsToMatrix(y): longitude > 360

Map of Response Times

Provide a map visualization of response times. Investigate whether the type of property affected (PROPERTY_USE_DESC) or fire severity (HIGHEST_LEVEL_DESC) play a role here.

Show a faceted choropleth map indicating how response times have developed over the years. What do you find?

library(rgdal)

## rgdal: version: 1.4-7, (SVN revision 845)
##  Geospatial Data Abstraction Library extensions to R successfully loaded
##  Loaded GDAL runtime: GDAL 2.2.3, released 2017/11/20
##  Path to GDAL shared files: C:/Users/jorda/OneDrive/Documents/R/win-library/3.6/rgdal/gdal
##  GDAL binary built with GEOS: TRUE 
##  Loaded PROJ.4 runtime: Rel. 4.9.3, 15 August 2016, [PJ_VERSION: 493]
##  Path to PROJ.4 shared files: C:/Users/jorda/OneDrive/Documents/R/win-library/3.6/rgdal/proj
##  Linking to sp version: 1.3-1

library(difftime)

## Error in library(difftime): there is no package called 'difftime'

borough_boundaries <- readOGR("borough_boundaries.geojson")

## OGR data source with driver: GeoJSON 
## Source: "C:\Users\jorda\OneDrive\Desktop\Data Visualization\borough_boundaries.geojson", layer: "borough_boundaries"
## with 5 features
## It has 4 fields

#get year var
building_fires_condensed$year <- substring(building_fires_condensed$INCIDENT_DATE_TIME, 7, 10)

fires2013 <- building_fires_condensed %>%
  filter(year == 2013)
fires2014 <- building_fires_condensed %>%
  filter(year == 2014)
fires2015 <- building_fires_condensed %>%
  filter(year == 2015)
fires2016 <- building_fires_condensed %>%
  filter(year == 2016)
fires2017 <- building_fires_condensed %>%
  filter(year == 2017)
fires2018 <- building_fires_condensed %>%
  filter(year == 2018)


map_final <- leaflet() %>%
  addTiles() %>% 
  addCircles(data = fires2013,
             lng = ~lon,
             lat = ~lat,
             color = color_pal,
             popup = variables1,
             radius = fires2013$TOTAL_INCIDENT_DURATION/100,
             group = "2013") %>%
  addCircles(data = fires2014,
             lng = ~lon,
             lat = ~lat,
             color = color_pal,
             popup = variables1,
             radius = fires2014$TOTAL_INCIDENT_DURATION/100,
             group = "2014") %>%
  addCircles(data = fires2015,
             lng = ~lon,
             lat = ~lat,
             color = color_pal,
             popup = variables1,
             radius = fires2015$TOTAL_INCIDENT_DURATION/100,
             group = "2015") %>%
  addCircles(data = fires2016,
             lng = ~lon,
             lat = ~lat,
             color = color_pal,
             popup = variables1,
             radius = fires2016$TOTAL_INCIDENT_DURATION/100,
             group = "2016") %>%
  addCircles(data = fires2017,
             lng = ~lon,
             lat = ~lat,
             color = color_pal,
             popup = variables1,
             radius = fires2017$TOTAL_INCIDENT_DURATION/100,
             group = "2017") %>%
  addCircles(data = fires2018,
             lng = ~lon,
             lat = ~lat,
             color = color_pal,
             popup = variables1,
             radius = fires2018$TOTAL_INCIDENT_DURATION/100,
             group = "2018") %>%
  addLayersControl(
    baseGroups = c("2013",
                   "2014",
                   "2015",
                   "2016",
                   "2017",
                   "2018"),
    position = "topleft",
    options = layersControlOptions(collapsed = FALSE))
  

map_final

Homework #2

Jordan Singh

3/22/2020